CN113211438A - Wheel type robot control method and system based on pre-aiming distance self-adaption - Google Patents
Wheel type robot control method and system based on pre-aiming distance self-adaption Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
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- B—PERFORMING OPERATIONS; TRANSPORTING
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- B25J9/00—Programme-controlled manipulators
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Abstract
The invention discloses a wheel type robot control method and system based on pre-aiming distance self-adaption. The control method comprises the following steps: setting a desired path, and repeatedly executing the following steps to enable the robot to travel along the desired path: s1, acquiring an optimal pre-aiming distance from the reference table based on the current path curvature and the current longitudinal speed, and acquiring a pre-aiming point corresponding to the optimal pre-aiming distance; s2, performing transverse control and/or longitudinal control based on the preview point; the transverse control is as follows: acquiring a target corner of a steering wheel of the robot based on a preview point, and controlling the steering wheel of the robot to deflect according to the target corner; the longitudinal control is as follows: and acquiring the total expected acceleration of the robot in the longitudinal direction, acquiring the target moment of each wheel of the robot according to the total expected acceleration of the robot in the longitudinal direction, and controlling each wheel to rotate according to the respective target moment. The optimal pre-aiming distance can be obtained from the reference table rapidly according to the real-time path curvature and the longitudinal speed in a self-adaptive manner, and the longitudinal and transverse motion cooperative control is carried out.
Description
Technical Field
The invention relates to the technical field of wheeled robot control, in particular to a wheeled robot control method and system based on pre-aiming distance self-adaption.
Background
Wheeled robots have been a hot spot of human research as an important branch of many fields of robots. Compared with a robot needing to be fixed at a certain position, the wheeled robot has stronger flexibility, can conveniently work in an environment which is difficult for human to reach, and helps human to explore unknown fields. For example, Chang' e three moon detector was successfully launched in 2013, 12 and 2, wherein the carried Jade rabbit number also becomes the first moon detection vehicle in China, and makes a great contribution to the aerospace industry in China. In addition, the wheeled robot has wide application potential in the logistics industry, the medical field and even the military field.
For wheeled robots widely applied in various fields, accurate motion control of the robots is a prerequisite for completing various tasks, and the accurate motion control mainly comprises research contents of transverse control and longitudinal control. The transverse control mainly studies the tracking ability of the robot to a given path, the longitudinal control mainly studies how the robot accurately tracks to drive at a desired speed, and the path tracking accuracy, the driving safety, the robot motion stability, the controller stability and the like are taken as main performance indexes.
From the control perspective, the design of the motion controller of the wheeled robot mainly comprises the aspects of robot dynamics model establishment, tracking deviation generation, control quantity generation and the like. At present, widely-applied control quantity generation methods mainly comprise a classical control method (represented by a PID algorithm), an optimal control method and the like. The PID controller has the advantages of simple design, convenience in calculation and the like, but the acquisition of the parameters of the PID controller is often focused on trial and error or experience estimation, and the migration application under different environments is difficult to realize. Optimal control often reduces the robot motion model to a linear steady-state system, relying on an accurate mathematical model in design, and thus controller stability and robustness can be affected when parameters are time-varying or external disturbances are present.
In addition, in the longitudinal and transverse control of the robot, the selection of the pre-aiming distance has great influence on the tracking precision, the steering portability and the motion stability of the robot. The shorter the preview distance is, the higher the tracking accuracy is, but steering smoothness and stability may be deteriorated. Therefore, how to reasonably select the pre-aiming distance in the movement process of the wheeled robot is very important.
Disclosure of Invention
The invention aims to at least solve the technical problems in the prior art, and particularly provides a wheeled robot control method and system based on pre-aiming distance self-adaption.
In order to achieve the above object, according to a first aspect of the present invention, there is provided a method for controlling a wheeled robot based on a preview distance adaptation, including: setting a desired path for the robot, and repeatedly executing the following steps to enable the robot to run along the desired path: step S1, acquiring a current path curvature and a current longitudinal speed, acquiring an optimal pre-aiming distance from a reference table based on the current path curvature and the current longitudinal speed, and acquiring a pre-aiming point corresponding to the optimal pre-aiming distance, wherein the pre-aiming distance is a longitudinal axis coordinate of the pre-aiming point under a robot coordinate system, and the optimal pre-aiming distance corresponding to different value combinations of the path curvature and the longitudinal speed is listed in the reference table; step S2, performing transverse control and/or longitudinal control based on the preview point; the transverse control is as follows: acquiring a target corner of a steering wheel of the robot based on the preview point, and controlling the steering wheel of the robot to deflect according to the target corner; the longitudinal control is as follows: and obtaining the total longitudinal expected acceleration of the robot according to the current longitudinal speed of the robot, the coordinate of the preview point and the expected longitudinal speed of the preview point, obtaining the target moment of each wheel of the robot according to the total longitudinal expected acceleration of the robot, and controlling each wheel to rotate according to the respective target moment.
The technical scheme is as follows: according to the control method, in the running process of the wheeled robot, the optimal pre-aiming distance can be obtained from the reference table rapidly in a self-adaptive manner according to the real-time path curvature and the longitudinal speed, and the optimal pre-aiming distance is utilized to control the transverse and/or longitudinal movement, so that the steering stability, the tracking precision and the movement stability are improved, and the cooperative control of the longitudinal and transverse movement of the robot is realized.
In a preferred embodiment of the present invention, the steering wheel of the robot is a front wheel.
The technical scheme is as follows: the steering control is facilitated.
In a preferred embodiment of the present invention, the method for establishing the reference table includes: setting value ranges for the pre-aiming distance, the path curvature and the longitudinal speed respectively, constructing a plurality of different value combinations of the path curvature and the longitudinal speed in the value ranges of the path curvature and the longitudinal speed, and obtaining the optimal pre-aiming distance corresponding to each value combination through a first method, wherein the first method comprises the following steps: step S11, setting a population scale, taking the preview distance as a position variable of the particle, taking the value range of the preview distance as a search space of the particle, initializing a historical optimal position and a fitness value corresponding to an initial value of the historical optimal position, and initializing a global optimal position and a fitness value corresponding to an initial value of the global optimal position; setting t as iteration times, and enabling an initial value of t to be 1; initializing the position and speed of each particle through a random function; in step S12, the t-th iteration includes: calculating the fitness value of each particle, and updating the global optimal position to the position of the particle with the minimum fitness value; if the minimum fitness is smaller than the fitness value corresponding to the historical optimal position, updating the historical optimal position to the position of the particle with the minimum fitness value, and corresponding the updated historical optimal position to the minimum fitness value, otherwise, not updating the historical optimal position; updating the position and velocity of each particle; step S13, if T is greater than or equal to T or the minimum fitness value of the current iteration converges to the preset fitness threshold, step S14 is entered, otherwise, T is made to be T +1, and step S12 is executed again; the T is a preset maximum iteration number; and step S14, finishing iteration, and taking the historical optimal position as the optimal pre-aiming distance corresponding to the value combination.
The technical scheme is as follows: because the pre-aiming distance, the path curvature and the longitudinal movement speed are difficult to accurately express by mathematical relations, the particle swarm optimization is adopted to realize the self-adaptive optimization of the pre-aiming distance to the path curvature and the movement speed, the path tracking precision is ensured, a reference table is established, the optimal pre-aiming distance can be quickly obtained by the table look-up mode, and the robot movement stability and the controller stability are ensured.
In a preferred embodiment of the present invention, the fitness value is obtained according to a fitness function, where the fitness function is:
wherein ,ω1Denotes J1Weight coefficient of (a), ω2Denotes J2Weight coefficient of (a), ω3Denotes J3Weight coefficient of (a), ω4Denotes J4Weight coefficient of (1), J1Representing a robot path tracking error cost function, J2Representing a directional error cost function, J3Representing the cost function of steering portability, J4Representing a lateral acceleration cost function; the robot path tracking error cost function
wherein ,Yref(t) represents the corresponding Y-axis coordinate of the desired path in the geodetic coordinate system at time t, Yreal(t) represents the corresponding Y-axis coordinate of the actual position point of the robot in the geodetic coordinate system at the moment of target rotation angle and/or target moment motion t obtained in step S2 with the position of the current particle as the optimal pre-aiming distance,
is a preset path deviation threshold value, tnRepresenting the time when the robot moves to the end point of the expected path according to the target rotation angle and/or the target moment obtained in the step S2 with the position of the current particle as the optimal pre-aiming distance; the directional error cost function
wherein ,
representing the robot yaw rate of the center of mass in the geodetic coordinate system at time t according to the target rotation angle and/or target moment motion obtained in step S2 with the position of the current particle as the optimal pre-aiming distance,
representing a preset robot centroid yaw angular velocity threshold, vxExpressing the longitudinal speed in the value combination; the steering portability cost function
wherein ,
indicating that the robot uses the position of the current particle as the optimal pre-aiming distance according to the target rotation angle and/or the target moment motion obtained in step S2 and the rotation angular velocity of the front wheel in the geodetic coordinate system at the moment t,
representing a preset robot front wheel rotation angular speed threshold; the lateral acceleration cost function
wherein ,
represents the lateral acceleration of the robot in the geodetic coordinate system at the time t according to the target rotation angle and/or the target moment motion obtained in step S2 with the position of the current particle as the optimal pre-aiming distance,
representing a preset robot lateral acceleration threshold.
The technical scheme is as follows: the fitness function can establish the corresponding relation between the pre-aiming distance and the curvature and the longitudinal speed of the path, and the particles selected by the fitness function can improve the tracking precision, the motion stability of the robot and the stability of the controller.
In a preferred embodiment of the present invention, in the step S2, the process of obtaining the target steered angle of the front wheels in the lateral control includes: step S21, establishing a single-axis motion model of the robot, wherein the single-axis motion model comprises three degrees of freedom under a robot coordinate system xoy, and the three degrees of freedom are translation along a longitudinal x axis, translation along a transverse y axis and rotation around the vertical direction, namely a yaw angle; step S22, obtaining the coordinate of the preview point in the robot coordinate system xoy according to the following formula
wherein ,xeThe coordinate of the longitudinal axis of the preview point under a robot coordinate system xoy is defined as a preview distance; y iseRepresenting the horizontal axis coordinate of the preview point under a robot coordinate system xoy, and defining the horizontal preview error;
representing a desired yaw angle of the robot at the preview point under the robot coordinate system xoy;
representing the coordinates of the pre-aiming point P in the geodetic coordinate system XOY, Xp、Yp、
Respectively representing the X-axis coordinate and the Y-axis coordinate of the preview point P in a geodetic coordinate system XOY, and the included angle between the tangent line of the preview point P and the X axis;
representing the coordinates of the current robot centroid in the geodetic coordinate system XOY, Xc、Yc、
Respectively representing an X-axis coordinate, a Y-axis coordinate and a yaw angle of the center of mass of the current robot in a geodetic coordinate system XOY; according to the coordinates of the preview point in the robot coordinate system xoy
And obtaining a single-point vision preview model of the robot by the single-axis motion model; and step S23, determining the target rotation angle of the front wheel of the robot by combining the single-point vision preview model and the slide film control.
The technical scheme is as follows: the transverse motion control of the robot is realized by controlling the corner of the front wheel, so that the wheeled robot can stably track the corner of the front wheel required by the expected path, and the robot is ensured to have smaller transverse deviation in the running process.
In a preferred embodiment of the present invention, in the geodetic coordinate system XOY, the single-axis motion model of the robot is represented as:
wherein ,δfIndicating a front wheel turning angle of the robot;
representing the longitudinal acceleration of the center of mass of the robot under a geodetic coordinate system XOY;
representing the transverse acceleration of the center of mass of the robot under a geodetic coordinate system XOY;
representing the yaw angular acceleration of the center of mass of the robot under the geodetic coordinate system XOY; variables of
Representing the transverse velocity of the center of mass of the robot under the geodetic coordinate system XOYThe degree of the magnetic field is measured,
representing the yaw velocity of the center of mass of the robot under a geodetic coordinate system XOY, m represents the mass of the robot, ClfRepresenting the longitudinal stiffness, C, of the front wheellrRepresenting the longitudinal stiffness of the rear wheel, sfRepresenting the slip ratio of the front wheel, srRepresents the slip ratio of the rear wheel; variables of
CcfRepresenting the front wheel side yaw stiffness, a representing the distance of the robot center of mass from the front axle; variables of
CcrRepresenting the lateral stiffness of the rear wheel, b representing the distance of the center of mass of the robot from the rear axle; variables of
sfRepresenting the front wheel slip ratio; variables of
IzRepresenting the moment of inertia of the robot about the z-axis; variables of
The technical scheme is as follows: the single-axis motion model can accurately and completely describe the motion posture of the robot by using only three degrees of freedom.
In a preferred embodiment of the present invention, the single-point visual preview model of the robot is:
wherein ,
indicating the rate of change of lateral deviation of the robot in the robot coordinate system,
representing the yaw velocity of the center of mass of the robot at the pre-aiming point in the robot coordinate system,
representing the yaw rate, v, of the current robot's center of mass in the geodetic coordinate systempRepresenting the desired longitudinal velocity, p, of the presigned point in the geodetic coordinate systempThe curvature of the path representing the pre-pointing point,
representing the lateral velocity at the robot's centroid in the geodetic coordinate system.
The technical scheme is as follows: and the subsequent target rotation angle calculation is facilitated through the single-point visual preview model.
In a preferred embodiment of the present invention, the step S23 of determining the target rotation angle of the front wheel of the robot by combining the single-point vision preview model and the slip film control specifically includes: step S231, obtaining a state equation of the controlled object X based on the sliding mode variable structure theory as follows:
wherein ,
respectively represents the longitudinal speed, the transverse speed and the yaw velocity of the mass center of the robot in a geodetic coordinate system at any time point in the process that the robot drives to the pre-aiming point,
indicating the rate of change of lateral deviation in the robot coordinate system,
means for deriving each component of X to obtain
The first coefficient matrix f (x) is:
representing a desired longitudinal acceleration of the preview point; the second coefficient matrix B is: b ═ B1 b2 b3 b4 b5 b6]T(ii) a The third coefficient matrix W is: w ═ 00000 vp]T(ii) a Step S232, with the horizontal preview error and the horizontal preview error change rate as the control target, the sliding mode surface can be designed as follows:
wherein c represents a synovial membrane parameter; s represents a slip film; step S233, when S → 0, the synovial membrane approach rate
The constant speed approach rate is set to satisfy the following relation:
wherein g represents a switching gain,
sat (·) represents a saturation function, Δ represents a boundary layer thickness, and a conversion coefficient q is 1/Δ; step S234, according to the relational expression
Obtain the equation
According to the equation
Obtaining the target rotation angle delta of the front wheel of the robot by the state equation of the control object XdComprises the following steps:
the technical scheme is as follows: the target corner obtained by combining a single-point vision preview model and a slip film control theory can better track an expected path in a transverse direction, and high-frequency buffeting of the slip film controller can be eliminated by using a saturation function in the slip film controller.
In a preferred embodiment of the present invention, the process of obtaining the target moment of the wheel in the longitudinal control includes: step A, acquiring the total longitudinal expected acceleration a of the robotrefComprises the following steps:
wherein ,vcRepresenting the current longitudinal velocity, v, of the robot in the geodetic coordinate systempRepresenting the desired longitudinal velocity, t, at the point of pre-aim in a geodetic coordinate systempIs that the robot maintains the current longitudinal velocity vcThe time of reaching the longitudinal coordinate position of the pre-aiming point in the geodetic coordinate system; step B, according to the total expected acceleration a of the robot in the longitudinal directionrefThe following equation is established:
wherein ,FlRepresenting the total desired longitudinal force of the robot, m representing the robot mass, a representing the road gradient, p representing the air density, CdDenotes the coefficient of air resistance, AwindRepresenting the windward area of the robot; step C, evenly distributing the torque to each wheel, wherein the target torque of each wheel is as follows:
where N represents the total number of wheels and R represents the wheel radius.
The technical scheme is as follows: longitudinal speed control is carried out based on the acceleration preview model, and independent average moment distribution is carried out on each wheel, so that longitudinal and transverse cooperative control of the wheeled robot is realized conveniently.
In order to achieve the above object, according to a second aspect of the present invention, the present invention provides a wheeled robot control system based on pre-aiming distance adaptation, including a state detection module mounted on a wheeled robot for detecting real-time position, attitude and speed of the robot during movement, and a wheel driving module and a controller; the controller is respectively connected with the state detection module and the wheel driving module; the controller controls the robot to move according to the wheel type robot control method based on the pre-aiming distance self-adaption.
The technical scheme is as follows: the control system can self-adaptively and quickly obtain the optimal pre-aiming distance from the reference table according to the real-time path curvature and the longitudinal speed in the running process of the wheeled robot, and utilizes the optimal pre-aiming distance to control the transverse and/or longitudinal motion, thereby improving the steering stability, the tracking precision and the motion stability and realizing the cooperative control of the longitudinal and transverse motion of the robot.
Drawings
Fig. 1 is a schematic flow chart of a wheeled robot control method based on pre-aiming distance adaptation according to an embodiment of the present invention;
FIG. 2 is a schematic view of a single axis motion model of a robot according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a single point visual preview model in accordance with an embodiment of the present invention;
FIG. 4 is a schematic flow chart of cooperative vertical and horizontal control in an application scenario of the present invention;
FIG. 5 is a schematic diagram of the interior of the controller in accordance with one embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
In the description of the present invention, it is to be understood that the terms "longitudinal", "lateral", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like, indicate orientations or positional relationships based on those shown in the drawings, and are used merely for convenience of description and for simplicity of description, and do not indicate or imply that the referenced devices or elements must have a particular orientation, be constructed in a particular orientation, and be operated, and thus, are not to be construed as limiting the present invention.
In the description of the present invention, unless otherwise specified and limited, it is to be noted that the terms "mounted," "connected," and "connected" are to be interpreted broadly, and may be, for example, a mechanical connection or an electrical connection, a communication between two elements, a direct connection, or an indirect connection via an intermediate medium, and specific meanings of the terms may be understood by those skilled in the art according to specific situations.
The invention discloses a wheeled robot control method based on pre-aiming distance self-adaptation, and in a preferred embodiment, as shown in fig. 1, the control method comprises the following steps: setting a desired path for the robot, and repeatedly executing the following steps to enable the robot to run along the desired path:
step S1, acquiring the curvature and the longitudinal speed of the current path, acquiring the optimal pre-aiming distance from a reference table based on the curvature and the longitudinal speed of the current path, and acquiring a pre-aiming point corresponding to the optimal pre-aiming distance, wherein the pre-aiming distance is the longitudinal axis coordinate of the pre-aiming point under the robot coordinate system, and the optimal pre-aiming distance corresponding to different value combinations of the curvature and the longitudinal speed of the path is listed in the reference table;
step S2, performing transverse control and/or longitudinal control based on the preview point;
the transverse control is as follows: acquiring a target corner of a steering wheel of the robot based on a preview point, and controlling the steering wheel of the robot to deflect according to the target corner;
the longitudinal control is as follows: and obtaining the total longitudinal expected acceleration of the robot according to the current longitudinal speed of the robot, the coordinate of the preview point and the expected longitudinal speed of the preview point, obtaining the target moment of each wheel of the robot according to the total longitudinal expected acceleration of the robot, and controlling each wheel to rotate according to the respective target moment.
In the present embodiment, in step S2, only the lateral control, only the longitudinal control, or both the lateral control and the longitudinal control may be executed simultaneously to realize the lateral-longitudinal cooperative control.
In the present embodiment, the steering wheel of the robot is preferably a front wheel. Preferably, each wheel of the robot obtains the target torque by means of evenly distributing the torque, so that the robot is convenient to control quickly and stably.
In this embodiment, in step S1, the optimal pre-aiming distance may be obtained from the reference table based on the current path curvature and the current longitudinal speed by direct reading or by interpolation to obtain a specific optimal pre-aiming distance value.
In a preferred embodiment, the method for establishing the reference table comprises the following steps: setting value ranges for the pre-aiming distance, the path curvature and the longitudinal speed respectively, constructing a plurality of different value combinations of the path curvature and the longitudinal speed in the value ranges of the path curvature and the longitudinal speed, wherein each resistance value combination corresponds to a group of path curvature values and longitudinal speed values, and obtaining the optimal pre-aiming distance corresponding to each value combination through a first method, wherein the first method comprises the following steps:
step S11, setting the population scale, namely the number of particles, taking the pre-aiming distance as a position variable of the particles, taking the value range of the pre-aiming distance as a search space of the particles, initializing the historical optimal position and the fitness value corresponding to the initial value of the historical optimal position, and initializing the global optimal position and the fitness value corresponding to the initial value of the global optimal position; preferably, the fitness value corresponding to the initial value of the historical optimal position and the fitness value corresponding to the initial value of the global optimal position may be set to 0. Setting t as iteration times, and enabling an initial value of t to be 1; initializing the position and speed of each particle through a random function;
in step S12, the t-th iteration includes:
calculating the fitness value of each particle, and updating the global optimal position to the position of the particle with the minimum fitness value; if the minimum fitness is smaller than the fitness value corresponding to the historical optimal position, updating the historical optimal position to the position of the particle with the minimum fitness value, and corresponding the updated historical optimal position to the minimum fitness value, otherwise, not updating the historical optimal position; updating the position and velocity of each particle; the position and speed updating formula of the particle can adopt the existing conventional updating formula, such as the reference website https: the updating formula disclosed in// blog, csdn, net/weixin, 40679412/article/details/80571854 will not be described in detail.
Step S13, if T is greater than or equal to T or the minimum fitness value of the current iteration converges to the preset fitness threshold, step S14 is entered, otherwise, T is made to be T +1, and step S12 is executed again; t is a preset maximum iteration number;
and step S14, finishing iteration, and taking the historical optimal position as the optimal pre-aiming distance corresponding to the value combination.
In this embodiment, the method for determining whether the minimum fitness value of the current iteration converges to the preset fitness threshold may be: and calculating the absolute value of the difference value between the minimum fitness value of the iteration and the fitness threshold, if the ratio of the absolute value to the fitness threshold is less than or equal to a preset proportional threshold, determining that the minimum fitness value of the iteration is converged to the preset fitness threshold, otherwise, determining that the minimum fitness value of the iteration is not converged to the preset fitness threshold.
In a preferred embodiment, the fitness value is obtained according to a fitness function, which is:
wherein ,ω1Denotes J1Weight coefficient of (a), ω2Denotes J2Weight coefficient of (a), ω3Denotes J3Weight coefficient of (a), ω4Denotes J4The weight coefficient of (2). Each weight coefficient identifies the relative importance of the corresponding index, e.g., if J1To J4Of equal importance, then ω1To omega4The same value is taken. J. the design is a square1Cost function representing robot path tracking errorNumber, J2Representing a directional error cost function, J3Representing the cost function of steering portability, J4Representing a lateral acceleration cost function;
robot path tracking error cost function
wherein ,Yref(t) represents the corresponding Y-axis coordinate of the desired path in the geodetic coordinate system at time t, Yreal(t) represents the corresponding Y-axis coordinate of the actual position point of the robot in the geodetic coordinate system at the moment of target rotation angle and/or target moment motion t obtained in step S2 with the position of the current particle as the optimal pre-aiming distance,
is a preset path deviation threshold value, tnRepresenting the time when the robot moves to the end point of the expected path according to the target rotation angle and/or the target moment obtained in the step S2 with the position of the current particle as the optimal pre-aiming distance; directional error cost function
wherein ,
representing the robot yaw rate of the center of mass in the geodetic coordinate system at time t according to the target rotation angle and/or target moment motion obtained in step S2 with the position of the current particle as the optimal pre-aiming distance,
representing a preset robot centroid yaw angular velocity threshold, vxExpressing the longitudinal speed in the value combination; steering portability cost function
wherein ,
indicating that the robot is currently particleAccording to the target rotation angle and/or the target moment motion obtained in step S2 as the optimal preview distance and the rotation angular velocity of the front wheel in the geodetic coordinate system at time t,
representing a preset robot front wheel rotation angular speed threshold; lateral acceleration cost function
wherein ,
represents the lateral acceleration of the robot in the geodetic coordinate system at the time t according to the target rotation angle and/or the target moment motion obtained in step S2 with the position of the current particle as the optimal pre-aiming distance,
representing a preset robot lateral acceleration threshold.
In the present embodiment, the path given in advance for the desired path may be an arbitrary curve or a straight line. Although there is no explicit relationship between the path curvature and the fitness function in the fitness function, different path curvatures correspond to different expected paths, which results in a cost function J of robot path tracking error when the control is performed according to the method in step S21Steering portability cost function J3Directional error cost function J2Lateral acceleration cost function J4The four functions have different function values and thus different path curvatures change the fitness function value.
In one preferred embodiment, the process of obtaining the target steered angle of the front wheels in the lateral control at step S2 includes:
step S21, establishing a single-axis motion model of the robot, where the wheeled robot is preferably a four-wheeled robot, and as shown in fig. 2, the single-axis motion model includes three degrees of freedom in a geodetic coordinate system XOY, where the three degrees of freedom are translation along a longitudinal X-axis, translation along a transverse Y-axis, and rotation around a vertical direction, i.e., a yaw angle; specifically, the four-wheel-drive wheeled robot single-axis motion model can be expressed as:
preferably, to simplify the mathematical derivation, the control quantity δ in the model representation is setfSeparating to obtain:
under the geodetic coordinate system XOY, the single-axis motion model of the robot is expressed as:
wherein ,δfIndicating a front wheel turning angle of the robot;
representing the longitudinal acceleration of the center of mass of the robot under a geodetic coordinate system XOY;
representing robot mass center
Acceleration in the lateral direction of (a);
representing robot mass center
Yaw angular acceleration of (a); variables of
Represents the transverse speed of the center of mass of the robot under a geodetic coordinate system XOY,
representing the yaw velocity of the center of mass of the robot under a geodetic coordinate system XOY, m represents the mass of the robot, ClfRepresenting the longitudinal stiffness, C, of the front wheellrRepresenting the longitudinal stiffness of the rear wheel, sfRepresenting the slip ratio of the front wheel, srRepresents the slip ratio of the rear wheel; variables of
CcfRepresenting the front wheel side yaw stiffness, a representing the distance of the robot center of mass from the front axle; variables of
CcrRear wheel side yaw stiffness, b represents the distance of the robot center of mass from the rear axle; variables of
Variables of
IzRepresenting the moment of inertia of the robot about the z-axis; variables of
Step S22, as shown in fig. 3, a single-point visual preview model is established for the wheeled robot, where XOY is a geodetic coordinate system, XOY is a coordinate system of the robot body, a centroid of the robot is used as a coordinate origin in XOY, and a point P and a point C respectively represent a preview point and a current position point of the robot.
The coordinates of the preview point under the robot coordinate system xoy are obtained according to the following formula
wherein ,xeThe coordinate of the longitudinal axis of the preview point under a robot coordinate system xoy is defined as a preview distance; y iseRepresenting the horizontal axis coordinate of the preview point under a robot coordinate system xoy, and defining the horizontal preview error;
representing a desired yaw angle of the robot at the preview point under the robot coordinate system xoy;
representing the coordinates of the pre-aiming point P in the geodetic coordinate system XOY, Xp、Yp、
Respectively representing the X-axis coordinate and the Y-axis coordinate of the preview point P in a geodetic coordinate system XOY, and the included angle between the tangent line of the preview point P and the X axis;
representing the coordinates of the current robot centroid in the geodetic coordinate system XOY, Xc、Yc、
Respectively representing an X-axis coordinate, a Y-axis coordinate and a yaw angle of the center of mass of the current robot in a geodetic coordinate system XOY;
according to the coordinates of the preview point in the robot coordinate system xoy
And obtaining a single-point vision preview model of the robot by the single-axis motion model;
preferably, the single-point visual aiming model of the robot is as follows:
wherein ,
indicating the rate of change of lateral deviation of the robot in the robot coordinate system,
representing the yaw velocity of the center of mass of the robot at the pre-aiming point in the robot coordinate system,
representing the yaw rate, v, of the current robot's center of mass in the geodetic coordinate systempIndicating the desired longitudinal velocity of the intended point in the geodetic coordinate system (since the transverse velocity of a typical robot is much less than the longitudinal velocity, v will be used herepDefined as longitudinal velocity), ρpThe curvature of the path representing the pre-pointing point,
representing the lateral velocity at the robot's centroid in the geodetic coordinate system.
And step S23, determining the target rotation angle of the front wheel of the robot by combining the single-point vision preview model and the slide film control.
In a preferred embodiment, the step S23 of determining the target rotation angle of the front wheel of the robot by combining the single-point vision preview model and the slip film control specifically includes:
step S231, obtaining a state equation of the controlled object X based on the sliding mode variable structure theory as follows:
wherein ,
respectively represents the longitudinal speed, the transverse speed and the yaw velocity of the mass center of the robot in a geodetic coordinate system at any time point in the process that the robot drives to the pre-aiming point,
indicating the rate of change of lateral deviation in the robot coordinate system,
means for deriving each component of X to obtain
The first coefficient matrix f (x) is:
representing a desired longitudinal acceleration of the preview point;
the second coefficient matrix B is: b ═ B1 b2 b3 b4 b5 b6]T；
The third coefficient matrix W is: w ═ 00000 vp]T；
Step S232, with the horizontal preview error and the horizontal preview error change rate as the control target, the sliding mode surface can be designed as follows:
step S233, when S → 0, the synovial membrane approach rate
The constant speed approach rate is set to satisfy the following relation: :
wherein g represents a switching gain,
sat (·) represents a saturation function, Δ represents a boundary layer thickness, and a conversion coefficient q is 1/Δ;
step S234, according to the relational expression
Obtain the equation
According to the equation
Obtaining the target rotation angle delta of the front wheel of the robot by the state equation of the control object XdComprises the following steps:
in the present embodiment, it is necessary to find the appropriate control amount s → 0, which requires the manual design of the controller for assurance
If true, then the state variable X is introduced. At this time
Resolving to s ═ s (0) e-gtWhen t → ∞, s → 0. The sat (-) function is introduced only to make the approach process smoother.
Showing a slip-form face. When s is equal to 0, there are
At this time ye＝ye(0)e-ctWhen t → ∞ is reached, ye→ 0. The control is achieved in such a way that the lateral deviation converges to 0. Therefore, g and c need to be positive numbers.
In the present embodiment, δdI.e. the desired target steering angle of the front wheel, the actual front wheel steering angle deltafShould be according to deltadAnd (4) changing.
The control rate of the sliding mode controller is as follows:
from the equation of state, it follows:
will be provided with
And
substituting equation (1) yields:
in a preferred embodiment, in order to realize the cooperative control of longitudinal and transverse motions of the wheeled robot, the invention designs a longitudinal speed tracking controller based on an acceleration preview model, and the process of obtaining the target moment of the wheel in the longitudinal control comprises the following steps:
step A, acquiring the total longitudinal expected acceleration a of the robotrefComprises the following steps:
step B, according to the total expected acceleration a of the robot in the longitudinal directionrefThe following equation is established:
wherein ,FlRepresenting the total desired longitudinal force of the robot, m representing the robot mass, a representing the road gradient, p representing the air density, CdDenotes the coefficient of air resistance, AwindRepresenting the windward area of the robot;
step C, in order to improve the control real-time, the invention adopts the mode of the average distribution of the moment to track and control the speed, the moment is averagely distributed to each wheel, and the target moment of each wheel is as follows:
where N represents the total number of wheels and R represents the wheel radius.
In an application scenario of the control method, in order to realize tracking control of a desired path, a wheel type robot nonlinear kinematics model (single-axis dynamic model) and a single-point vision preview model are established to obtain a robot longitudinal and transverse motion control model, and then longitudinal and transverse motion cooperative control is realized based on the nonlinear control model, wherein a specific flow diagram is shown in fig. 4 and comprises the following steps:
the method comprises the steps of firstly, obtaining expected paths and expected speed information of the wheeled robot in a geodetic coordinate system. Specifically, a smooth motion track can be planned for the wheeled robot according to different application environments, and then the track is interpolated to obtain a series of track posture points.
Step two, acquiring current pose information and speed information of the wheeled robot
Specifically, a vision sensor, a millimeter wave radar, a three-axis inertial measurement unit, and the like may be fixed on the wheeled robot platform to detect real-time position, attitude, and speed information during the movement of the robot.
Step three, acquiring the pose information of the pre-aiming point on the expected path in the geodetic coordinate system
And calculating the pose information of the preview point on the expected path under the robot body coordinate system based on the single-point vision preview model
wherein xe and yeRespectively the pre-aim distance and the lateral deviation.
Specifically, based on the expected trajectory in the geodetic coordinate system obtained in the foregoing, coordinates of a series of expected trajectory points in the robot body coordinate system can be calculated here by means of coordinate transformation.
Step four, calculating a target turning angle delta of a front wheel required by the wheeled robot to stably track the expected pathd。
Specifically, a robot transverse motion controller based on a sliding mode variable structure theory is designed by analyzing and calculating a wheel type robot nonlinear dynamics model and a visual preview model and utilizing a Lyapunov stability criterion.
And fifthly, calculating the adaptive pre-aiming distance for different path curvatures and speeds based on the particle swarm algorithm.
Specifically, the optimal pre-aiming distance considering the path tracking precision, the robot motion stability and the controller stability is calculated by adopting a particle swarm algorithm, a reference table of the pre-aiming distance changing along with the path curvature and the robot speed is manufactured, and the optimal pre-aiming distance is obtained by looking up the table during actual calculation.
And step six, calculating expected torque required by robot speed tracking based on acceleration preview.
Specifically, the design of the longitudinal controller adopts a torque average distribution mode to obtain the expected torque T of the robot based on the acceleration preview model, so that the longitudinal and transverse cooperative control of the wheeled robot is realized.
The invention also discloses a wheel type robot control system based on the pre-aiming distance self-adaption, and in a preferred embodiment, the control system comprises a state detection module, a wheel driving module and a controller, wherein the state detection module is arranged on the wheel type robot and is used for detecting the real-time position, the attitude and the speed of the robot in the motion process; the controller is respectively connected with the state detection module and the wheel driving module; and the controller controls the robot to move according to the wheel type robot control method based on the pre-aiming distance self-adaption.
In the present embodiment, the state detection module is preferably, but not limited to, a vision sensor mounted on the robot, a millimeter wave radar, a three-axis inertial measurement unit. The wheel drive module is preferably, but not limited to, an existing in-wheel motor drive module.
In the embodiment, the controller obtains a robot longitudinal and transverse motion control model by establishing a wheeled robot nonlinear kinematics model and a vision preview model, and then realizes the longitudinal and transverse motion cooperative control based on the nonlinear control model.
In the present embodiment, the internal structure of the controller is as shown in fig. 5, and includes a lateral controller, a longitudinal controller, a desired track path planning module, an optimal preview distance determination module, a visual preview model unit, and the like.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the invention have been shown and described, it will be understood by those of ordinary skill in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.
Claims (10)
1. A control method of a wheeled robot based on pre-aiming distance self-adaptation is characterized in that a desired path is set for the robot, and the following steps are repeatedly executed to enable the robot to drive along the desired path:
step S1, acquiring a current path curvature and a current longitudinal speed, acquiring an optimal pre-aiming distance from a reference table based on the current path curvature and the current longitudinal speed, and acquiring a pre-aiming point corresponding to the optimal pre-aiming distance, wherein the pre-aiming distance is a longitudinal axis coordinate of the pre-aiming point under a robot coordinate system, and the optimal pre-aiming distance corresponding to different value combinations of the path curvature and the longitudinal speed is listed in the reference table;
step S2, performing transverse control and/or longitudinal control based on the preview point;
the transverse control is as follows: acquiring a target corner of a steering wheel of the robot based on the preview point, and controlling the steering wheel of the robot to deflect according to the target corner;
the longitudinal control is as follows: and obtaining the total longitudinal expected acceleration of the robot according to the current longitudinal speed of the robot, the coordinate of the preview point and the expected longitudinal speed of the preview point, obtaining the target moment of each wheel of the robot according to the total longitudinal expected acceleration of the robot, and controlling each wheel to rotate according to the respective target moment.
2. The method for controlling the wheeled robot based on the pre-aiming distance adaptation as claimed in claim 1, wherein the steering wheels of the robot are front wheels.
3. A method for controlling a wheeled robot based on adaptive preview distance according to claim 1 or 2, wherein the method for establishing the reference table comprises:
setting value ranges for the pre-aiming distance, the path curvature and the longitudinal speed respectively, constructing a plurality of different value combinations of the path curvature and the longitudinal speed in the value ranges of the path curvature and the longitudinal speed, and obtaining the optimal pre-aiming distance corresponding to each value combination through a first method, wherein the first method comprises the following steps:
step S11, setting a population scale, taking the preview distance as a position variable of the particle, taking the value range of the preview distance as a search space of the particle, initializing a historical optimal position and a fitness value corresponding to an initial value of the historical optimal position, and initializing a global optimal position and a fitness value corresponding to an initial value of the global optimal position; setting t as iteration times, and enabling an initial value of t to be 1; initializing the position and speed of each particle through a random function;
in step S12, the t-th iteration includes:
calculating the fitness value of each particle, and updating the global optimal position to the position of the particle with the minimum fitness value; if the minimum fitness is smaller than the fitness value corresponding to the historical optimal position, updating the historical optimal position to the position of the particle with the minimum fitness value, and corresponding the updated historical optimal position to the minimum fitness value, otherwise, not updating the historical optimal position; updating the position and velocity of each particle;
step S13, if T is greater than or equal to T or the minimum fitness value of the current iteration converges to the preset fitness threshold, step S14 is entered, otherwise, T is made to be T +1, and step S12 is executed again; the T is a preset maximum iteration number;
and step S14, finishing iteration, and taking the historical optimal position as the optimal pre-aiming distance corresponding to the value combination.
4. The method for controlling the wheeled robot based on the pre-aiming distance self-adaptation as claimed in claim 3, wherein the fitness value is obtained according to a fitness function, and the fitness function is as follows:
wherein ,ω1Denotes J1Weight coefficient of (a), ω2Denotes J2Weight coefficient of (a), ω3Denotes J3Weight coefficient of (a), ω4Denotes J4Weight coefficient of J1Representing a robot path tracking error cost function, J2Representing a directional error cost function, J3Representing the cost function of steering portability, J4Representing a lateral acceleration cost function;
the robot path tracking error cost function
wherein ,Yref(t) represents the corresponding Y-axis coordinate of the desired path in the geodetic coordinate system at time t, Yreal(t) represents the corresponding Y-axis coordinate of the actual position point of the robot in the geodetic coordinate system at the moment of target rotation angle and/or target moment motion t obtained in step S2 with the position of the current particle as the optimal pre-aiming distance,
is a preset path deviation threshold value, tnRepresenting the time when the robot moves to the end point of the expected path according to the target rotation angle and/or the target moment obtained in the step S2 with the position of the current particle as the optimal pre-aiming distance;
the directional error cost function
wherein ,
representing the robot yaw rate of the center of mass in the geodetic coordinate system at time t according to the target rotation angle and/or target moment motion obtained in step S2 with the position of the current particle as the optimal pre-aiming distance,
representing a preset robot centroid yaw angular velocity threshold, vxExpressing the longitudinal speed in the value combination;
the steering portability cost function
wherein ,
indicating that the robot uses the position of the current particle as the optimal pre-aiming distance according to the target rotation angle and/or the target moment motion obtained in step S2 and the rotation angular velocity of the front wheel in the geodetic coordinate system at the moment t,
representing a preset robot front wheel rotation angular speed threshold;
the lateral acceleration cost function
wherein ,
represents the lateral acceleration of the robot in the geodetic coordinate system at the time t according to the target rotation angle and/or the target moment motion obtained in step S2 with the position of the current particle as the optimal pre-aiming distance,
representing a preset robot lateral acceleration threshold.
5. The method for controlling a wheeled robot based on adaptive preview distance according to claim 2, wherein the step S2 of obtaining the target turning angle of the front wheels in the lateral control comprises:
step S21, establishing a single-axis motion model of the robot, wherein the single-axis motion model comprises three degrees of freedom under a robot coordinate system xoy, and the three degrees of freedom are translation along a longitudinal x axis, translation along a transverse y axis and rotation around the vertical direction, namely a yaw angle;
step S22, obtaining the coordinate of the preview point in the robot coordinate system xoy according to the following formula
wherein ,xeThe coordinate of the longitudinal axis of the preview point under a robot coordinate system xoy is defined as a preview distance; y iseRepresenting the horizontal axis coordinate of the preview point under a robot coordinate system xoy, and defining the horizontal preview error;
representing a desired yaw angle of the robot at the preview point under the robot coordinate system xoy;
representing the coordinates of the pre-aiming point P in the geodetic coordinate system XOY, Xp、Yp、
Respectively representing the X-axis coordinate and the Y-axis coordinate of the preview point P in a geodetic coordinate system XOY, and the included angle between the tangent line of the preview point P and the X axis;
representing the current robot centroid in the geodetic coordinate system XOYCoordinate of (2), Xc、Yc、
Respectively representing an X-axis coordinate, a Y-axis coordinate and a yaw angle of the center of mass of the current robot in a geodetic coordinate system XOY;
according to the coordinates of the preview point in the robot coordinate system xoy
And obtaining a single-point vision preview model of the robot by the single-axis motion model;
and step S23, determining the target rotation angle of the front wheel of the robot by combining the single-point vision preview model and the slide film control.
6. The method for controlling the wheeled robot based on the pre-aiming distance adaptation as claimed in claim 5, wherein under a geodetic coordinate system XOY, the single-axis motion model of the robot is represented as:
wherein ,δfIndicating a front wheel turning angle of the robot;
representing the longitudinal acceleration of the center of mass of the robot under a geodetic coordinate system XOY;
representing the transverse acceleration of the center of mass of the robot under a geodetic coordinate system XOY;
representing the yaw angular acceleration of the center of mass of the robot under the geodetic coordinate system XOY; variables of
Represents the transverse speed of the center of mass of the robot under a geodetic coordinate system XOY,
representing the yaw velocity of the center of mass of the robot under a geodetic coordinate system XOY, m represents the mass of the robot, ClfRepresenting the longitudinal stiffness, C, of the front wheellrRepresenting the longitudinal stiffness of the rear wheel, sfRepresenting the slip ratio of the front wheel, srRepresents the slip ratio of the rear wheel; variables of
CcfRepresenting the front wheel side yaw stiffness, a representing the distance of the robot center of mass from the front axle; variables of
CcrRepresenting the lateral stiffness of the rear wheel, b representing the distance of the center of mass of the robot from the rear axle; variables of
Variables of
IzRepresenting the moment of inertia of the robot about the z-axis; variables of
7. The method for controlling the wheeled robot based on the preview distance adaptation as claimed in claim 6, wherein the single-point vision preview model of the robot is as follows:
wherein ,
indicating the rate of change of lateral deviation of the robot in the robot coordinate system,
representing the yaw velocity of the center of mass of the robot at the pre-aiming point in the robot coordinate system,
representing the yaw rate, v, of the current robot's center of mass in the geodetic coordinate systempRepresenting the desired longitudinal velocity, p, of the presigned point in the geodetic coordinate systempThe curvature of the path representing the pre-pointing point,
representing the lateral velocity at the robot's centroid in the geodetic coordinate system.
8. The method for controlling a wheeled robot based on preview distance adaptation as claimed in claim 7, wherein said step S23 combining the single point vision preview model and the synovial membrane control to determine the target rotation angle of the front wheels of the robot specifically comprises:
step S231, obtaining a state equation of the controlled object X based on the sliding mode variable structure theory as follows:
wherein ,
respectively indicating the driving direction of the robotThe longitudinal speed, the transverse speed and the yaw velocity of the center of mass of the robot in the geodetic coordinate system at any time point in the point process,
indicating the rate of change of lateral deviation in the robot coordinate system,
means for deriving each component of X to obtain
The first coefficient matrix f (x) is:
representing a desired longitudinal acceleration of the preview point;
the second coefficient matrix B is: b ═ B1 b2 b3 b4 b5 b6]T；
The third coefficient matrix W is: w ═ 00000 vp]T；
Step S232, with the horizontal preview error and the horizontal preview error change rate as the control target, the sliding mode surface can be designed as follows:
step S233, when S → 0, the synovial membrane approach rate
The constant speed approach rate is set to satisfy the following relation:
wherein g represents a switching gain,
sat (·) represents a saturation function, Δ represents a boundary layer thickness, and a conversion coefficient q is 1/Δ;
step S234, according to the relational expression
Obtain the equation
According to the equation
Obtaining the target rotation angle delta of the front wheel of the robot by the state equation of the control object XdComprises the following steps:
9. the method for controlling the wheeled robot based on the pre-aiming distance adaptation as claimed in claim 1, wherein the process of obtaining the target moment of the wheels in the longitudinal control comprises:
step A, acquiring the total longitudinal expected acceleration a of the robotrefComprises the following steps:
step B, according to the total expected acceleration a of the robot in the longitudinal directionrefThe following equation is established:
wherein ,FlRepresenting the total desired longitudinal force of the robot, m representing the robot mass, a representing the road gradient, p representing the air density, CdDenotes the coefficient of air resistance, AwindRepresenting the windward area of the robot;
step C, evenly distributing the torque to each wheel, wherein the target torque of each wheel is as follows:
where N represents the total number of wheels and R represents the wheel radius.
10. A wheeled robot control system based on pre-aiming distance self-adaptation is characterized by comprising a state detection module, a wheel driving module and a controller, wherein the state detection module is arranged on a wheeled robot and is used for detecting the real-time position, the attitude and the speed of the robot in the motion process; the controller is respectively connected with the state detection module and the wheel driving module; the controller controls the robot to move according to the wheel type robot control method based on the pre-aiming distance adaptation in any one of claims 1 to 9.
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